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Super-Resolution for Remote Sensing Applications Using Deep Learning Techniques

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Title: Super-Resolution for Remote Sensing Applications Using Deep Learning Techniques
Description: Satellite image processing is crucial in detecting vegetation, clouds, and other atmospheric applications. Due to sensor limitations and pre-processing, remotely sensed satellite images may have interpretability concerns as to specific portions of the image, making it hard to recognise patterns or objects and posing the risk of losing minute details in the image. Existing imaging processors and optical components are expensive to counterfeit, have interpretability issues, and are not necessarily viable in real applications. This book exploits the usage of deep learning (DL) components in feature extraction to boost the minute details of images and their classification implications to tackle such problems. It shows the importance of super-resolution in improving the spatial details of images and aiding digital aerial photography in pan-sharpening, detecting signatures correctly, and making precise decisions with decision-making tools.
Authors: G. Rohith, Author, G. Lakshmi Sutha, Author
Resource Type: eBook.
Subjects: Deep learning (Machine learning), Remote-sensing images--Data processing
Categories: TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems
Database: eBook Collection (EBSCOhost)
FullText Links:
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Text:
  Availability: 0
Header DbId: nlebk
DbLabel: eBook Collection (EBSCOhost)
An: 3570773
RelevancyScore: 1116
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 1116.28857421875
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  Data: Super-Resolution for Remote Sensing Applications Using Deep Learning Techniques
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  Data: Satellite image processing is crucial in detecting vegetation, clouds, and other atmospheric applications. Due to sensor limitations and pre-processing, remotely sensed satellite images may have interpretability concerns as to specific portions of the image, making it hard to recognise patterns or objects and posing the risk of losing minute details in the image. Existing imaging processors and optical components are expensive to counterfeit, have interpretability issues, and are not necessarily viable in real applications. This book exploits the usage of deep learning (DL) components in feature extraction to boost the minute details of images and their classification implications to tackle such problems. It shows the importance of super-resolution in improving the spatial details of images and aiding digital aerial photography in pan-sharpening, detecting signatures correctly, and making precise decisions with decision-making tools.
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RecordInfo BibRecord:
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    Classifications:
      – Code: 621.367
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Deep learning (Machine learning)
        Type: general
      – SubjectFull: Remote-sensing images--Data processing
        Type: general
    Titles:
      – TitleFull: Super-Resolution for Remote Sensing Applications Using Deep Learning Techniques
        Type: main
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            NameFull: G. Rohith, Author
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            NameFull: G. Lakshmi Sutha, Author
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            NameFull: G. Rohith, Author
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2023
            – D: 15
              M: 03
              Type: profile
              Y: 2023
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              Value: 9781527591349
            – Type: isbn-electronic
              Value: 9781527591356
          Titles:
            – TitleFull: Super-Resolution for Remote Sensing Applications Using Deep Learning Techniques
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